In this task, we will be performing exploratory data analysis on the dataset "GlobalTerrorism" and try to find out the hot zone of terrorism. Also, we will derive the security issues and various insights.
# Importing all the libraries needed in this notebook
import math
import warnings
import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import plotly.offline as py
import plotly.graph_objs as go
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
global_terror = pd.read_csv('globalterrorismdb_0718dist.csv',encoding='ISO-8859-1')
global_terror.head() # first five values of the dataset
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | ... | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.970000e+11 | 1970 | 7 | 2 | NaN | 0 | NaN | 58 | Dominican Republic | 2 | ... | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 1 | 1.970000e+11 | 1970 | 0 | 0 | NaN | 0 | NaN | 130 | Mexico | 1 | ... | NaN | NaN | NaN | NaN | PGIS | 0 | 1 | 1 | 1 | NaN |
| 2 | 1.970010e+11 | 1970 | 1 | 0 | NaN | 0 | NaN | 160 | Philippines | 5 | ... | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 3 | 1.970010e+11 | 1970 | 1 | 0 | NaN | 0 | NaN | 78 | Greece | 8 | ... | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 4 | 1.970010e+11 | 1970 | 1 | 0 | NaN | 0 | NaN | 101 | Japan | 4 | ... | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
5 rows × 135 columns
global_terror.columns
Index(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',
'resolution', 'country', 'country_txt', 'region',
...
'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG',
'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related'],
dtype='object', length=135)
global_terror.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country','provstate':'state',
'region_txt':'Region','attacktype1_txt':'AttackType','target1':'Target','nkill':'Killed',
'nwound':'Wounded','summary':'Summary','gname':'Group','targtype1_txt':'Target_type',
'weaptype1_txt':'Weapon_type','motive':'Motive'},inplace=True)
# Important data for further processing
global_terror=global_terror[['Year','Month','Day','Country','state','Region','city','latitude','longitude','AttackType','Killed',
'Wounded','Target','Summary','Group','Target_type','Weapon_type','Motive']]
global_terror.head()
| Year | Month | Day | Country | state | Region | city | latitude | longitude | AttackType | Killed | Wounded | Target | Summary | Group | Target_type | Weapon_type | Motive | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970 | 7 | 2 | Dominican Republic | NaN | Central America & Caribbean | Santo Domingo | 18.456792 | -69.951164 | Assassination | 1.0 | 0.0 | Julio Guzman | NaN | MANO-D | Private Citizens & Property | Unknown | NaN |
| 1 | 1970 | 0 | 0 | Mexico | Federal | North America | Mexico city | 19.371887 | -99.086624 | Hostage Taking (Kidnapping) | 0.0 | 0.0 | Nadine Chaval, daughter | NaN | 23rd of September Communist League | Government (Diplomatic) | Unknown | NaN |
| 2 | 1970 | 1 | 0 | Philippines | Tarlac | Southeast Asia | Unknown | 15.478598 | 120.599741 | Assassination | 1.0 | 0.0 | Employee | NaN | Unknown | Journalists & Media | Unknown | NaN |
| 3 | 1970 | 1 | 0 | Greece | Attica | Western Europe | Athens | 37.997490 | 23.762728 | Bombing/Explosion | NaN | NaN | U.S. Embassy | NaN | Unknown | Government (Diplomatic) | Explosives | NaN |
| 4 | 1970 | 1 | 0 | Japan | Fukouka | East Asia | Fukouka | 33.580412 | 130.396361 | Facility/Infrastructure Attack | NaN | NaN | U.S. Consulate | NaN | Unknown | Government (Diplomatic) | Incendiary | NaN |
# Checking for the null values
global_terror.isnull().sum()
Year 0 Month 0 Day 0 Country 0 state 421 Region 0 city 434 latitude 4556 longitude 4557 AttackType 0 Killed 10313 Wounded 16311 Target 636 Summary 66129 Group 0 Target_type 0 Weapon_type 0 Motive 131130 dtype: int64
global_terror.info() # Returns the concise summary
<class 'pandas.core.frame.DataFrame'> RangeIndex: 181691 entries, 0 to 181690 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year 181691 non-null int64 1 Month 181691 non-null int64 2 Day 181691 non-null int64 3 Country 181691 non-null object 4 state 181270 non-null object 5 Region 181691 non-null object 6 city 181257 non-null object 7 latitude 177135 non-null float64 8 longitude 177134 non-null float64 9 AttackType 181691 non-null object 10 Killed 171378 non-null float64 11 Wounded 165380 non-null float64 12 Target 181055 non-null object 13 Summary 115562 non-null object 14 Group 181691 non-null object 15 Target_type 181691 non-null object 16 Weapon_type 181691 non-null object 17 Motive 50561 non-null object dtypes: float64(4), int64(3), object(11) memory usage: 25.0+ MB
print("Country with the most attacks:",global_terror['Country'].value_counts().idxmax())
print("City with the most attacks:",global_terror['city'].value_counts().index[1]) #as first entry is 'unknown'
print("Region with the most attacks:",global_terror['Region'].value_counts().idxmax())
print("Year with the most attacks:",global_terror['Year'].value_counts().idxmax())
print("Month with the most attacks:",global_terror['Month'].value_counts().idxmax())
print("Group with the most attacks:",global_terror['Group'].value_counts().index[1])
print("Most Attack Types:",global_terror['AttackType'].value_counts().idxmax())
Country with the most attacks: Iraq City with the most attacks: Baghdad Region with the most attacks: Middle East & North Africa Year with the most attacks: 2014 Month with the most attacks: 5 Group with the most attacks: Taliban Most Attack Types: Bombing/Explosion
from wordcloud import WordCloud
from scipy import signal
cities = global_terror.state.dropna(False)
plt.subplots(figsize=(20,10))
wordcloud = WordCloud(background_color = 'black',
width = 500,
height = 400).generate(' '.join(cities))
plt.axis('off')
plt.imshow(wordcloud)
plt.show()
global_terror['Year'].value_counts(dropna = False).sort_index()
1970 651 1971 471 1972 568 1973 473 1974 581 1975 740 1976 923 1977 1319 1978 1526 1979 2662 1980 2662 1981 2586 1982 2544 1983 2870 1984 3495 1985 2915 1986 2860 1987 3183 1988 3721 1989 4324 1990 3887 1991 4683 1992 5071 1994 3456 1995 3081 1996 3058 1997 3197 1998 934 1999 1395 2000 1814 2001 1906 2002 1333 2003 1278 2004 1166 2005 2017 2006 2758 2007 3242 2008 4805 2009 4721 2010 4826 2011 5076 2012 8522 2013 12036 2014 16903 2015 14965 2016 13587 2017 10900 Name: Year, dtype: int64
x_year = global_terror['Year'].unique()
y_count_years = global_terror['Year'].value_counts(dropna = False).sort_index()
plt.figure(figsize = (18,10))
sns.barplot(x = x_year,
y = y_count_years,
palette = 'rocket')
plt.xticks(rotation = 50)
plt.xlabel('Attack Year')
plt.ylabel('Number of Attacks Each Year')
plt.title('Attack In Years')
plt.show()
plt.subplots(figsize=(20,10))
sns.countplot(x='Year', data=global_terror, palette='RdYlGn_r', edgecolor=sns.color_palette("YlOrBr", 10))
plt.xticks(rotation=50)
plt.title('Number Of Terrorist Activities Each Year')
plt.show()
pd.crosstab(global_terror.Year, global_terror.Region).plot(kind='area',figsize=(20,10))
plt.title('Terrorist Activities By Region In Each Year')
plt.ylabel('Number of Attacks')
plt.show()
global_terror['Wounded'] = global_terror['Wounded'].fillna(0).astype(int)
global_terror['Killed'] = global_terror['Killed'].fillna(0).astype(int)
global_terror['Casualities'] = global_terror['Killed'] + global_terror['Wounded']
# Top 50 worst terrorist attacks
global_terror1 = global_terror.sort_values(by='Casualities',ascending=False)[:50]
heat=global_terror1.pivot_table(index='Country',columns='Year',values='Casualities')
heat.fillna(0,inplace=True)
heat.head()
| Year | 1982 | 1984 | 1987 | 1992 | 1994 | 1995 | 1996 | 1997 | 1998 | 2001 | ... | 2005 | 2006 | 2007 | 2008 | 2009 | 2013 | 2014 | 2015 | 2016 | 2017 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||||||
| Afghanistan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 536.0 | 0.0 | 584.0 |
| Algeria | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 450.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Chad | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 1161.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Egypt | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 438.0 |
| Ethiopia | 0.0 | 0.0 | 0.0 | 500.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 21 columns
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
colorscale = [[0, '#edf8fb'], [.3, '#00BFFF'], [.6, '#8856a7'], [1, '#810f7c']]
heatmap = go.Heatmap(z=heat.values, x=heat.columns, y=heat.index, colorscale=colorscale)
data = [heatmap]
layout = go.Layout(
title='Top 50 Worst Terror Attacks in History from 1982 to 2017',
xaxis = dict(ticks='', nticks=20),
yaxis = dict(ticks='')
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='heatmap',show_link=False)
global_terror.Country.value_counts()[:21]
Iraq 24636 Pakistan 14368 Afghanistan 12731 India 11960 Colombia 8306 Philippines 6908 Peru 6096 El Salvador 5320 United Kingdom 5235 Turkey 4292 Somalia 4142 Nigeria 3907 Thailand 3849 Yemen 3347 Spain 3249 Sri Lanka 3022 United States 2836 Algeria 2743 France 2693 Egypt 2479 Lebanon 2478 Name: Country, dtype: int64
plt.subplots(figsize=(20,10))
sns.barplot(x=global_terror['Country'].value_counts()[:20].index, y=global_terror['Country'].value_counts()[:20].values, palette='Blues_d')
plt.title('Top Countries Affected')
plt.xlabel('Countries')
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()
import folium
from folium.plugins import MarkerCluster
filterYear = global_terror['Year'] == 2001
filterData = global_terror[filterYear] # filter data
# filterData.info()
reqFilterData = filterData.loc[:,'city':'longitude'] # get the required fields
reqFilterData = reqFilterData.dropna() # drop NaN values in latitude and longitude
reqFilterDataList = reqFilterData.values.tolist()
# reqFilterDataList
map = folium.Map(location = [0, 50], tiles='CartoDB positron', zoom_start=2)
markerCluster = folium.plugins.MarkerCluster().add_to(map)
for point in range(0, len(reqFilterDataList)):
folium.Marker(location=[reqFilterDataList[point][1],reqFilterDataList[point][2]],
popup = reqFilterDataList[point][0]).add_to(markerCluster)
map
From the above map, we can depict that the maximum attacks carried out in the year 2001 was on the African Continent, almost 1325 attacks. Then, the continent South America faced the highest number of attacks, i.e. 258.
global_terror.Group.value_counts()[1:20]
Taliban 7478 Islamic State of Iraq and the Levant (ISIL) 5613 Shining Path (SL) 4555 Farabundo Marti National Liberation Front (FMLN) 3351 Al-Shabaab 3288 New People's Army (NPA) 2772 Irish Republican Army (IRA) 2671 Revolutionary Armed Forces of Colombia (FARC) 2487 Boko Haram 2418 Kurdistan Workers' Party (PKK) 2310 Basque Fatherland and Freedom (ETA) 2024 Communist Party of India - Maoist (CPI-Maoist) 1878 Maoists 1630 Liberation Tigers of Tamil Eelam (LTTE) 1606 National Liberation Army of Colombia (ELN) 1561 Tehrik-i-Taliban Pakistan (TTP) 1351 Palestinians 1125 Houthi extremists (Ansar Allah) 1062 Al-Qaida in the Arabian Peninsula (AQAP) 1020 Name: Group, dtype: int64
test = global_terror[global_terror.Group.isin(['Shining Path (SL)','Taliban','Islamic State of Iraq and the Levant (ISIL)'])]
test.Country.unique()
array(['Peru', 'Bolivia', 'Colombia', 'Argentina', 'Brazil', 'Mexico',
'Afghanistan', 'Pakistan', 'Syria', 'Iraq', 'Turkey', 'Tunisia',
'Lebanon', 'Turkmenistan', 'Israel', 'Belgium', 'Egypt', 'Libya',
'Saudi Arabia', 'West Bank and Gaza Strip', 'France', 'Bahrain',
'Jordan', 'Somalia', 'Germany', 'Yemen', 'Philippines', 'Malaysia',
'Indonesia', 'Russia', 'Georgia', 'United Kingdom', 'Iran',
'Australia'], dtype=object)
global_terror_df_group = global_terror.dropna(subset=['latitude','longitude'])
global_terror_df_group = global_terror_df_group.drop_duplicates(subset=['Country','Group'])
terrorist_groups = global_terror.Group.value_counts()[1:8].index.tolist()
global_terror_df_group = global_terror_df_group.loc[global_terror_df_group.Group.isin(terrorist_groups)]
print(global_terror_df_group.Group.unique())
["New People's Army (NPA)" 'Irish Republican Army (IRA)' 'Shining Path (SL)' 'Farabundo Marti National Liberation Front (FMLN)' 'Taliban' 'Al-Shabaab' 'Islamic State of Iraq and the Levant (ISIL)']
map = folium.Map(location=[50, 0], tiles="CartoDB positron", zoom_start=2)
markerCluster = folium.plugins.MarkerCluster().add_to(map)
for i in range(0,len(global_terror_df_group)):
folium.Marker([global_terror_df_group.iloc[i]['latitude'],global_terror_df_group.iloc[i]['longitude']],
popup='Group:{}<br>Country:{}'.format(global_terror_df_group.iloc[i]['Group'],
global_terror_df_group.iloc[i]['Country'])).add_to(map)
map
m1 = folium.Map(location=[50, 0], tiles="CartoDB positron", zoom_start=2)
marker_cluster = MarkerCluster(
name='clustered icons',
overlay=True,
control=False,
icon_create_function=None
)
for i in range(0,len(global_terror_df_group)):
marker=folium.Marker([global_terror_df_group.iloc[i]['latitude'],global_terror_df_group.iloc[i]['longitude']])
popup='Group:{}<br>Country:{}'.format(global_terror_df_group.iloc[i]['Group'],
global_terror_df_group.iloc[i]['Country'])
folium.Popup(popup).add_to(marker)
marker_cluster.add_child(marker)
marker_cluster.add_to(m1)
folium.TileLayer('openstreetmap').add_to(m1)
#folium.TileLayer('Mapbox Bright').add_to(m1)
folium.TileLayer('cartodbdark_matter').add_to(m1)
folium.TileLayer('stamentoner').add_to(m1)
folium.LayerControl().add_to(m1)
m1
global_terror.head()
| Year | Month | Day | Country | state | Region | city | latitude | longitude | AttackType | Killed | Wounded | Target | Summary | Group | Target_type | Weapon_type | Motive | Casualities | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970 | 7 | 2 | Dominican Republic | NaN | Central America & Caribbean | Santo Domingo | 18.456792 | -69.951164 | Assassination | 1 | 0 | Julio Guzman | NaN | MANO-D | Private Citizens & Property | Unknown | NaN | 1 |
| 1 | 1970 | 0 | 0 | Mexico | Federal | North America | Mexico city | 19.371887 | -99.086624 | Hostage Taking (Kidnapping) | 0 | 0 | Nadine Chaval, daughter | NaN | 23rd of September Communist League | Government (Diplomatic) | Unknown | NaN | 0 |
| 2 | 1970 | 1 | 0 | Philippines | Tarlac | Southeast Asia | Unknown | 15.478598 | 120.599741 | Assassination | 1 | 0 | Employee | NaN | Unknown | Journalists & Media | Unknown | NaN | 1 |
| 3 | 1970 | 1 | 0 | Greece | Attica | Western Europe | Athens | 37.997490 | 23.762728 | Bombing/Explosion | 0 | 0 | U.S. Embassy | NaN | Unknown | Government (Diplomatic) | Explosives | NaN | 0 |
| 4 | 1970 | 1 | 0 | Japan | Fukouka | East Asia | Fukouka | 33.580412 | 130.396361 | Facility/Infrastructure Attack | 0 | 0 | U.S. Consulate | NaN | Unknown | Government (Diplomatic) | Incendiary | NaN | 0 |
# Total Number of people killed in terror attack
killData = global_terror.loc[:,'Killed']
print('Number of people killed by terror attack:', int(sum(killData.dropna())))# drop the NaN values
Number of people killed by terror attack: 411868
# Let's look at what types of attacks these deaths were made of.
attackData = global_terror.loc[:,'AttackType']
# attackData
typeKillData = pd.concat([attackData, killData], axis=1)
typeKillData.head()
| AttackType | Killed | |
|---|---|---|
| 0 | Assassination | 1 |
| 1 | Hostage Taking (Kidnapping) | 0 |
| 2 | Assassination | 1 |
| 3 | Bombing/Explosion | 0 |
| 4 | Facility/Infrastructure Attack | 0 |
typeKillFormatData = typeKillData.pivot_table(columns='AttackType', values='Killed', aggfunc='sum')
typeKillFormatData
| AttackType | Armed Assault | Assassination | Bombing/Explosion | Facility/Infrastructure Attack | Hijacking | Hostage Taking (Barricade Incident) | Hostage Taking (Kidnapping) | Unarmed Assault | Unknown |
|---|---|---|---|---|---|---|---|---|---|
| Killed | 160297 | 24920 | 157321 | 3642 | 3718 | 4478 | 24231 | 880 | 32381 |
typeKillFormatData.info()
<class 'pandas.core.frame.DataFrame'> Index: 1 entries, Killed to Killed Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Armed Assault 1 non-null int32 1 Assassination 1 non-null int32 2 Bombing/Explosion 1 non-null int32 3 Facility/Infrastructure Attack 1 non-null int32 4 Hijacking 1 non-null int32 5 Hostage Taking (Barricade Incident) 1 non-null int32 6 Hostage Taking (Kidnapping) 1 non-null int32 7 Unarmed Assault 1 non-null int32 8 Unknown 1 non-null int32 dtypes: int32(9) memory usage: 152.0+ bytes
labels = typeKillFormatData.columns.tolist()
transposed = typeKillFormatData.T
values = transposed.values.flatten().tolist() # Flatten the 2D array
fig, ax = plt.subplots(figsize=(20, 20), subplot_kw=dict(aspect="equal"))
plt.pie(values, startangle=90, autopct='%.2f%%')
plt.title('Types Of Terrorist Attacks That Cause Deaths', fontsize=30)
plt.legend(labels, loc='upper right', bbox_to_anchor=(1.3, 0.9), fontsize=15) # Location of the legend
plt.show()
global_terror.head(2)
| Year | Month | Day | Country | state | Region | city | latitude | longitude | AttackType | Killed | Wounded | Target | Summary | Group | Target_type | Weapon_type | Motive | Casualities | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970 | 7 | 2 | Dominican Republic | NaN | Central America & Caribbean | Santo Domingo | 18.456792 | -69.951164 | Assassination | 1 | 0 | Julio Guzman | NaN | MANO-D | Private Citizens & Property | Unknown | NaN | 1 |
| 1 | 1970 | 0 | 0 | Mexico | Federal | North America | Mexico city | 19.371887 | -99.086624 | Hostage Taking (Kidnapping) | 0 | 0 | Nadine Chaval, daughter | NaN | 23rd of September Communist League | Government (Diplomatic) | Unknown | NaN | 0 |
# Number of Killed in Terrorist Attacks by Countries
countryData = global_terror.loc[:,'Country']
# countyData
countryKillData = pd.concat([countryData, killData], axis=1)
countryKillFormatData = countryKillData.pivot_table(columns='Country', values='Killed', aggfunc='sum')
countryKillFormatData
| Country | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | ... | Vietnam | Wallis and Futuna | West Bank and Gaza Strip | West Germany (FRG) | Western Sahara | Yemen | Yugoslavia | Zaire | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Killed | 39384 | 42 | 11066 | 0 | 3043 | 0 | 490 | 37 | 23 | 30 | ... | 1 | 0 | 1500 | 97 | 1 | 8776 | 119 | 324 | 70 | 154 |
1 rows × 205 columns
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
labels = countryKillFormatData.columns.tolist()
labels = labels[:50] #50 bar provides nice view
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[:50]
values = [int(i[0]) for i in values] # convert float to int
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange'] # color list for bar chart bar color
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=30)
plt.xlabel('Countries', fontsize = 30)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number of People Killed By Countries', fontsize = 30)
# print(fig_size)
plt.show()
labels = countryKillFormatData.columns.tolist()
labels = labels[50:101]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[50:101]
values = [int(i[0]) for i in values]
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=20
fig_size[1]=20
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of People Killed By Countries', fontsize = 30)
plt.show()
labels = countryKillFormatData.columns.tolist()
labels = labels[101:152]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[101:152]
values = [int(i[0]) for i in values]
colors = ['red', 'blue', 'brown', 'orange', 'purple', 'yellow', 'black', 'green', 'gray', 'magenta']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of people Killed By Countries', fontsize = 20)
plt.show()
labels = countryKillFormatData.columns.tolist()
labels = labels[152:206]
index = np.arange(len(labels))
transpoze = countryKillFormatData.T
values = transpoze.values.tolist()
values = values[152:206]
values = [int(i[0]) for i in values]
colors = ['red', 'green', 'blue', 'purple', 'yellow', 'brown', 'black', 'gray', 'magenta', 'orange']
fig, ax = plt.subplots(1, 1)
ax.yaxis.grid(True)
fig_size = plt.rcParams["figure.figsize"]
fig_size[0]=25
fig_size[1]=25
plt.rcParams["figure.figsize"] = fig_size
plt.bar(index, values, color = colors, width = 0.9)
plt.ylabel('Killed People', fontsize=20)
plt.xlabel('Countries', fontsize = 20)
plt.xticks(index, labels, fontsize=20, rotation=90)
plt.title('Number Of people Killed By Countries', fontsize = 20)
plt.show()
So after different type of analyzation ,Overall terrorism is suddenly increased from 2010 and I have ranked the Hot zone in terms of terrorist activities in all regions -
Iraq
Overall having highest Number of terrorist rate and sudden increase in terrorist activity after year 2010
Libya
Overall having low terrorism activity, Second Highest in terms of successful terrorist activities which takes place in this country and an increased in terrorism rate after 2010.
Yemen
Overall having low terrorism activity, Third Highest in terms of successful terrorist activities which takes place in this country and an increased in terrorism rate after 2010.
Afghanistan
Overall having 1st Highest terrorist rates and 1st highest in terms of extended terrorist rates and a very high increase in terrorism rate after 2010.
Pakistan
Overall having 2nd highest terrorist rates but there is a good decrease in terrorist rates if we consider the 20s century and an increase in terrorism rate after 2010 but not as that of increase like Afghanistan and India.
India
Overall having 3rd highest terrorist rates but if we consider extended terrorist rate so this country is 2nd highest and a very high increase in terrorism rate after 2010.
Nigeria
Overall having low terrorism activity but 1st Highest in terms of extended terrorist activities which takes place in this country and 2nd in terms of increased of terrorism rate after 2010.
Somalia
Overall having low terrorism activity but 2nd Highest in terms of extended terrorist activities which takes place in this country and 1st in terms of increased of terrorism rate after 2010.
Sudan
Overall having low terrorism activity but 3rd Highest in terms of extended terrorist activities which takes place in this country and a bit increase in terrorism rate after 2010.
- More security surveillance required at Iraq.
- Noticing the trends of terrorism activities , hugely populated regions suffer major kill ratios. This must be controlled with strict border rules
- Impose strict rules in controlling the explosions produce worldwide limiting the number of supplies #High alert at densly populated locations in each city as it is noticed that terrorists target is private citizens and property